Master of Social Work - Traditional
The traditional Master of Social Work (MSW) program at the University at Buffalo is des...
M.S. in Engineering Science - Data Science
Buffalo, New York
INTAKE: Jan & Aug
Program Overview
The M.S. in Engineering Science (Data Science) at UB is a 30-credit hour, coursework-focused program that can typically be completed in one calendar year of full-time study, though it can extend to 1.5 to 2 years. The program emphasizes a sound basic theory with a strong focus on the practical aspects of data, computing, and analysis. Graduates are prepared to serve the analytics needs of employers in a wide range of application areas. The degree offers flexibility through the use of electives and a culminating project, allowing students to specialize their learning. Classes are often modestly sized, emphasizing best classroom practices while utilizing online resources to enhance the learning experience.
STEM Designated: Yes, the M.S. in Engineering Science (Data Science) program at the University at Buffalo is STEM-designated. This is a significant benefit for international students, as it allows for an Optional Practical Training (OPT) extension of 24 months beyond the initial 12 months, totaling up to 36 months of work authorization in the U.S. after graduation. This designation highlights the program's rigorous scientific, technical, and analytical curriculum.
Curriculum: The 30-credit curriculum for the M.S. in Engineering Science (Data Science) is structured to provide a comprehensive education in big data and analysis. The typical course plan for full-time students includes four core courses in the first semester focusing on "Math and Stats Basics," followed by three core courses and one elective in the second semester, and finally one "Data Science Survey" course and a Project/Capstone in the third semester. Core courses often include "Probability Theory for Data Science," "Numerical Mathematics for Computing and Data Scientists," "Statistical Data Mining," "Programming and Database Fundamentals for Data Scientists," "Machine Learning," and "Data Models Query Language." This curriculum combines solid theoretical foundations with hands-on training.
Research Focus: While the M.S. in Engineering Science (Data Science) is an applied program, it is grounded in the University at Buffalo's strong research capabilities in computational and data sciences. Students gain knowledge and expertise in scalable data-driven discovery. The program's culminating project allows students to apply their learned skills to real-world data challenges, demonstrating their ability to derive insights and propose solutions. UB has been a research pioneer in these areas, with faculty possessing extensive expertise and utilizing world-leading facilities like the Center for Computational Research, which offers unmatched resources for big computing and data. This research ecosystem enhances the program's practical and theoretical depth.
Industry Engagement: The M.S. in Engineering Science (Data Science) program at UB is highly focused on preparing graduates for the high-demand job market in data and computing sciences. The curriculum emphasizes practical aspects of data, computing, and analysis, ensuring graduates are well-prepared to serve the analytics needs of employers. The program actively fosters industry engagement through various means, including a speaker series that invites industry experts to present on cutting-edge topics in analytics. Students also have opportunities for internships with community organizations and industry partners, providing valuable hands-on work experience and networking opportunities. These industry collaborations ensure the curriculum remains relevant and prepares students for immediate impact.
Global Perspective: The M.S. in Engineering Science (Data Science) at UB inherently fosters a global perspective by addressing the universal applicability of data science across diverse sectors and international contexts. The principles and methodologies taught are essential for solving complex problems with global implications, from analyzing international market trends to addressing global health challenges. As a major public research university, UB attracts a diverse international student body and faculty, enriching the classroom environment with varied cultural viewpoints and different approaches to data challenges from around the world. This exposure prepares graduates to work effectively in multinational teams and contribute to data-driven solutions on a global scale.
Location
Buffalo, New York
Score
IELTS 6.5
Tuition Fee
USD 28210
Postgraduate Entry Requirements
Academic Qualifications: Applicants for postgraduate programs typically require a minimum academic achievement of 70% or above in their bachelor's degree.
English Language Proficiency:
- IELTS: Overall band score of 6.5 or 7.0 with a minimum of 6.0 in each component.
- TOEFL: Overall score of 90 or higher.
- PTE: Overall score of 61 or higher.
- DET (Duolingo English Test): Minimum score of 120.
The University at Buffalo (UB) offers a variety of scholarships and financial aid opportunities specifically aimed at supporting international students who wish to pursue their studies in the United States. These scholarships are designed to reward academic excellence, leadership, and community involvement, helping to make education more affordable for talented students worldwide.
Merit-Based Scholarships: UB provides competitive merit scholarships to outstanding international undergraduate and graduate students. Awards such as the International Student Academic Excellence Scholarship recognize high-achieving students based on their academic records, standardized test scores, and extracurricular involvement.
Graduate Fellowships and Assistantships: Graduate international students can apply for teaching assistantships, research assistantships, and fellowships which offer tuition remission and stipends. These opportunities allow students to gain valuable teaching and research experience while offsetting the cost of their education.
Departmental Scholarships: Many academic departments at UB offer scholarships tailored to students in specific programs or fields of study. These awards may consider academic merit, research interests, or financial need.
External Scholarships: UB encourages international students to explore external scholarship options from private organizations, governments, and international foundations that support study in the U.S. The university’s International Student Services office provides guidance on identifying and applying for such funding sources.
Graduates with an M.S. in Engineering Science (Data Science) from UB are highly competitive for a wide range of data-centric roles across various industries, given their blend of theoretical knowledge and practical application skills.
Data Scientist: Collect, analyze, and interpret large, complex datasets to identify trends, build predictive models, and provide actionable insights for business or research.
Machine Learning Engineer: Design, build, and deploy machine learning algorithms and models, often focusing on developing intelligent systems and applications.
Data Analyst: Extract, clean, and interpret data to create reports, dashboards, and visualizations that help organizations understand performance and make informed decisions.
Business Intelligence (BI) Developer/Analyst: Develop and implement BI solutions, including data warehousing, reporting tools, and dashboards, to support business strategy and operations.
Data Engineer: Design, construct, install, and maintain scalable data pipelines and architectures, ensuring data availability and quality for analysis and machine learning.
Quantitative Analyst (Quant): Apply mathematical and statistical models to analyze financial markets, manage risk, and develop trading strategies, particularly in the finance industry.
AI Specialist/Engineer: Focus on developing artificial intelligence solutions, which may involve natural language processing, computer vision, or robotics applications.
Statistical Modeler: Develop and validate statistical models for various applications, including risk assessment, forecasting, and experimental design in fields like finance, healthcare, or marketing.
Research Scientist (Data Science): Conduct advanced research in data science, developing new algorithms, methodologies, and tools, often in academic institutions or corporate R&D labs.
Consultant (Data & Analytics): Provide expert advice and solutions to clients across different industries on data strategy, analytics implementation, and leveraging data for competitive advantage.
